Paper Info
Title
Dispatch Optimisation In O2o On-Demand Service With Crowd-Sourced And In-House Drivers
Abstract
O2O (Online to Offline) services enable customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organise offline delivery services. In practice, three types of workforce, namely, in-house, full-time, and part-time crowd-sourced drivers, coexist in the system while exhibiting different characteristics. This situation creates challenges for the management of order assignment and routing. In particular, we study three settings in response to different driver preferences: the guaranteed minimum daily number of orders for full-time drivers; the maximally allowed number of orders per trip; and the detour proportion for part-time drivers. This paper aims to provide a method for O2O platforms to optimise order assignment and routing, considering these designs about driver preferences. We further validate our model and study managerial insights using real datasets. Specifically, the results show that among all designed parameters for the O2O on-demand delivery system, two parameters - the maximally allowed number of orders per trip and the detour proportion - are critical for the design. Moreover, we find that incentive mechanisms for inexperienced and experienced drivers are different because of their service capacities. The managerial insights are expected to guide practitioners.
Year
DOI
Venue
2021
10.1080/00207543.2020.1800120
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Keywords
DocType
Volume
Dispatching, routing, crowd-sourcing, O2O, on-demand service
Journal
59
Issue
ISSN
Citations 
20
0020-7543
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jiawei Tao100.68
Hongyan Dai200.34
Hai Jiang3275.25
Weiwei Chen412512.21